Black Boxes and the Role of Modeling in Environmental Policy Making

Modeling is essential for modern science, and science-based policies are directly affected by the reliability of model outputs. Artificial intelligence and increasing computational performance have improved the accuracy and capability of model simulations, but often at the expense of a rational understanding of the systems involved. When the internal logic of a model is hidden, it is often referred to as a black box. The lack of transparency in black box models, artificial intelligence based among them, can potentially affect the trust on science driven policy making. Here, we suggest that a broader discussion is needed to address the implications of black box approaches on the reliability of scientific advice used for policy making. We argue that participatory methods can bridge the gap between increasingly complex scientific methods and the people affected by their interpretations.